Despite efforts to make safer roads, vehicles and restraints, auto crashes are the leading cause of accidental deaths among individuals between the ages of 65-74. Cognitive-motor impairments are major contributors to this death rate. In order to reduce this major source of mortality and morbidity, better means of testing and re-training driving competence are essential. These new means need to improve the effectiveness, for example, of clinical researchers assessing pharmacological and behavioral interventions, of clinicians determining whether patients should resume driving following CNS assault, and of insurance or regulatory agencies such as the Department of Motor Vehicles (DMV). As one specific example, DMV on-road testing is subjective, unreliable, minimally challenging, inconsistent across centers, costly, potentially dangerous, and not validated in terms of false negatives and true negatives. Virtual reality driving simulation is a potentially viable "better means" alternative that could be reliable, challenging, consistent, objective, low risk, discriminating, and relatively low cost. While simulation has been extensively used in the military, airline, and game industries, it has not been similarly embraced for driving testing and training for several reasons: 1) there are no generally accepted standards that allow comparability across facilities, 2) prevalence of simulation sickness among seniors limits general use, 3) data demonstrating that it parallels on-road driving skills is lacking, and 4) costs are high. With our multi-disciplinary team of engineering, behavioral and mathematical scientists, in consultation with our advisory board, and in collaboration with Virginia's DMV, we propose to develop a simulator that circumvents the above barriers. The Model T2 (Testing and Training) simulator will be reliable, inexpensive, modular, easy to repair and upgrade, effective, and standardized. Model T2 will administer sophisticated driving scenarios and will utilize advanced data processing algorithms, resulting in reliable, sensitive and specific driving performance measures. In addition, Model T2 will administer an applied driving knowledge test. Achieving this, and its extensive deployment, would allow a common means of testing and training driving competence, making rapid advancements in this critical field possible. This work will be built upon the foundation of our over 80 years' combined driving simulation experience, our prototype that simulates actual roads, and our work on simulation sickness. This six-month SBER feasibility Phase 1 study will include three parallel interacting processes: 1) optimizing simulator platform components (sophisticated, durable, inexpensive), 2) refining, validating and extending scenario software, and 3) refining reliable performance variables and testing the feasibility of employing the Model T2 in the applied setting of a DMV field office. A subsequent Phase 2 would focus on building the production prototype of the final product, generating normative data, expanding its applicability to re-training older drivers, initiating mass production, and marketing.